You can visualize data in Jupyter notebooks using the HERE Inspector package. The inspector allows you to visualize data from various sources, including the HERE platform. It provides an interface designed to support multiple backends. Currently, supported backends are:
Note: The map widget used in this visualization is the default widget provided by the HERE map service. In order to use the widget, please ensure that you have set the environment variable "LS_API_KEY" with your HERE API key. Additionally, please note that custom styling options such as zoom, center, basemap, theme, and colors are not currently supported by the Kepler.gl library.
Capabilities include:
Display interactive maps
Set basemap
Add layers
Center and zoom
Display interactive charts
Simple programmatic interface
Support for displaying heatmaps, choropleths, markers, clustering, and styling
The simplest usage of Inspector is to call its inspect function for immediate one-line visualization of georeferenced data within a Jupyter notebook.
Inspect Single Dataset
Note: To use a specific map widget, you will need to specify the appropriate inspector_class and api_key options. For example, to use the Ipyleaflet map widget, you would set options.inspector_class = IpyleafletInspector and options.api_key = None. Similarly, to use the HERE Map or Kepler.gl map widgets, you would set options.inspector_class = HEREMapInspector or options.inspector_class = KeplerInspector, respectively and set api_key as per the requirement of widgets.
Here Map Widget
import json
from here.inspector import inspect
# Dataset type = GeoJSON file# This file contains polygon features describing neighborhood boundaries in Chicagowithopen('sample_datasets/neighborhood_boundaries.geojson')as f:
area_boundaries = json.load(f)#By default if no inspector class is set , sdk will take HERE Map widget for map visualization.#user can manually set it as follows:-from here.inspector import options,HEREMapInspector
options.inspector_class = HEREMapInspector
options.api_key ='Your actual HERE API key'
inspect(area_boundaries)
Figure 1. Here Map Widget
Ipyleaflet Widget
import json
from here.inspector import inspect
# Dataset type = GeoJSON file# This file contains polygon features describing neighborhood boundaries in Chicagowithopen('sample_datasets/neighborhood_boundaries.geojson')as f:
area_boundaries = json.load(f)from here.inspector import options,IpyleafletInspector
options.inspector_class = IpyleafletInspector
options.api_key =None
inspect(area_boundaries)
Figure 2. Ipyleaflet Widget
Kepler Widget
import json
from here.inspector import inspect
# Dataset type = GeoJSON file# This file contains polygon features describing neighborhood boundaries in Chicagowithopen('sample_datasets/neighborhood_boundaries.geojson')as f:
area_boundaries = json.load(f)from here.inspector import options,KeplerInspector
options.inspector_class = KeplerInspector
options.api_key =None
inspect(area_boundaries)
Figure 3. Kepler Widget
Inspect Multiple Datasets
import geopandas as gpd
# Dataset type = GeoDataFrame# This dataframe contains all Chicago Transit Authority rail station entrance locations
cta_df = gpd.read_file('sample_datasets/cta_entrances.geojson')
cta_df.head()
name
agency
line
geometry
0
18th
CTA
Pink Line
POINT (-87.669144 41.857849)
1
35th/Archer
CTA
Orange Line
POINT (-87.680632 41.829274)
2
95th-Dan Ryan
CTA
Red Line
POINT (-87.62441 41.722729)
3
Adams/Wabash
CTA
Brown, Purple, Orange, Pink, Green Lines
POINT (-87.625997 41.879715)
4
Addison
CTA
Blue Line
POINT (-87.718406 41.946604)
Here Map Widget
from here.inspector import options,HEREMapInspector
options.inspector_class = HEREMapInspector
options.api_key ='Your actual HERE API key'
inspect(layers={'Neighborhoods': area_boundaries,'Stations': cta_df})
Kepler Widget ). The Inspector.backend function provides access to all advanced functionalities of the rendering backend, allowing unlimited customization.
Please see the Tutorial Notebooks named ExploreInspector_ipyleaflet.ipynb , ExploreInspector_kepler.ipynb and ExploreInspector_HereMapWidgetForJupyter.ipynb for comprehensive examples of all visualization options.